Correlation Between DIeteren Group and Lithia Motors
Can any of the company-specific risk be diversified away by investing in both DIeteren Group and Lithia Motors at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining DIeteren Group and Lithia Motors into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DIeteren Group SA and Lithia Motors, you can compare the effects of market volatilities on DIeteren Group and Lithia Motors and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in DIeteren Group with a short position of Lithia Motors. Check out your portfolio center. Please also check ongoing floating volatility patterns of DIeteren Group and Lithia Motors.
Diversification Opportunities for DIeteren Group and Lithia Motors
0.51 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between DIeteren and Lithia is 0.51. Overlapping area represents the amount of risk that can be diversified away by holding DIeteren Group SA and Lithia Motors in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Lithia Motors and DIeteren Group is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on DIeteren Group SA are associated (or correlated) with Lithia Motors. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Lithia Motors has no effect on the direction of DIeteren Group i.e., DIeteren Group and Lithia Motors go up and down completely randomly.
Pair Corralation between DIeteren Group and Lithia Motors
Assuming the 90 days trading horizon DIeteren Group is expected to generate 1.4 times less return on investment than Lithia Motors. In addition to that, DIeteren Group is 1.05 times more volatile than Lithia Motors. It trades about 0.07 of its total potential returns per unit of risk. Lithia Motors is currently generating about 0.1 per unit of volatility. If you would invest 24,258 in Lithia Motors on September 24, 2024 and sell it today you would earn a total of 10,342 from holding Lithia Motors or generate 42.63% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
DIeteren Group SA vs. Lithia Motors
Performance |
Timeline |
DIeteren Group SA |
Lithia Motors |
DIeteren Group and Lithia Motors Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with DIeteren Group and Lithia Motors
The main advantage of trading using opposite DIeteren Group and Lithia Motors positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DIeteren Group position performs unexpectedly, Lithia Motors can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Lithia Motors will offset losses from the drop in Lithia Motors' long position.DIeteren Group vs. Copart Inc | DIeteren Group vs. Zhongsheng Group Holdings | DIeteren Group vs. CarMax Inc | DIeteren Group vs. Penske Automotive Group |
Lithia Motors vs. Copart Inc | Lithia Motors vs. Zhongsheng Group Holdings | Lithia Motors vs. CarMax Inc | Lithia Motors vs. DIeteren Group SA |
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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